Abstract
Our approach in text independent Speaker Verification (SV) proposes to integrate different aspects of the speech signal which convey information about the speaker’s identity using Graphical Models (GM). Prosodic, spectral and source information obtained from the residue of linear prediction analysis are modeled in a probabilistic framework with a system based on Bayesian Networks (BN). The structure, or conditional independencies between the variables, is learned directly from the data using two different algorithms. In particular, the interpretation and comparation of the structures is presented. Some experiments conducted on the NIST 2003 one speaker text-independent data base have been conducted to demonstrate the feasibility of this approach.
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© 2005 Springer-Verlag Berlin Heidelberg
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Sánchez-Soto, E., Sigelle, M., Chollet, G. (2005). Graphical Models for Text-Independent Speaker Verification. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_26
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DOI: https://doi.org/10.1007/11520153_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27441-4
Online ISBN: 978-3-540-31886-6
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Bayesian Network
- Bayesian Information Criterion
- Speech Signal
- Conditional Independency
- Minimal Description Length
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
